DroneRF dataset: A dataset of drones for RF-based detection, classification and identification

Abstract

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article "RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database" (Al-Sa'd et al., 2019).

Keywords: Anti-drone systems; Classification; Drone identification; UAV detection.

Fig. 1

Experimental setup for the RF database development. The Bebop drone is shown on the middle, the NI-USRP RF receivers are shown on the right and are connected to the laptops, shown on the left, via the PCIe connectors.

Fig. 2

NI USRP-2943R RF receiver .

Fig. 3

a: Front panel of the LabVIEW program installed on the laptops to capture the drones' RF communication . b: Block diagram of LabVIEW program .

Fig. 4

RF activities plots with normalized amplitudes between −1 and 1. (a) shows segment number 13 of the acquired RF background activities, (b) shows segment number 10 of the acquired Phantom drone activity.

Fig. 5

Different snippets of RF activities for different flight modes for the Bebop drone with normalized amplitude between 1 and -1. Each figure shows the segment number 1 of each flight mode.

Fig. 6

Different snippets of RF activities for different flight modes for the AR drone with normalized amplitude between 1 and -1. Each figure shows the segment number 1 of each flight mode.

Fig. 7

Experiments to record drones RF signatures organized in a tree manner consisting of three levels. The horizontal dashed red lines define the levels. BUI is a Binary Unique Identifier for each component to be used in labelling .